ArticleFrontiers in network physiology2025
Magnitude-constrained optimal chaotic desynchronization of neural populations.
Article in Frontiers in network physiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Model-based design of subthalamic nucleus neurons using hybrid optimization.Journal of neurophysiology · 2026Article
- Magnitude-constrained optimal chaotic desynchronization of neural populations.Frontiers in network physiology · 2025Article
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3 authors.
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Abstract
In this paper, we calculate magnitude-constrained optimal stimuli for desynchronizing a population of neurons by maximizing the Lyapunov exponent for the phase difference between pairs of neurons while simultaneously minimizing the energy which is used. This theoretical result informs the way optimal inputs can be designed for deep brain stimulation in cases where there is a biological or electronic constraint on the amount of current that can be applied. By exploring a range of parameter values, we characterize how the constraint magnitude affects the Lyapunov exponent and energy usage. Finally, we demonstrate the efficacy of this approach by considering a computational model for a population of neurons with repeated event-triggered optimal inputs.
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